Comportamiento de dataframe

Dataset statistics

Number of variables	15
Number of observations	79
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	18.1 KiB
Average record size in memory	235.1 B

Variable types

Categorical	2
Numeric	13

Alerts

`NEUMONIA` is highly correlated with `EDAD` and 11 other fields	High correlation
`EDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`DIABETES` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`EPOC` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`ASMA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`INMUSUPR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`HIPERTENSION` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`OTRA_COM` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CARDIOVASCULAR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`OBESIDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`RENAL_CRONICA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`TABAQUISMO` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CLASIFICACION_FINAL` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`NEUMONIA` is highly correlated with `EDAD` and 11 other fields	High correlation
`EDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`DIABETES` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`EPOC` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`ASMA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`INMUSUPR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`HIPERTENSION` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`OTRA_COM` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CARDIOVASCULAR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`OBESIDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`RENAL_CRONICA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`TABAQUISMO` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CLASIFICACION_FINAL` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`NEUMONIA` is highly correlated with `EDAD` and 11 other fields	High correlation
`EDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`DIABETES` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`EPOC` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`ASMA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`INMUSUPR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`HIPERTENSION` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`OTRA_COM` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CARDIOVASCULAR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`OBESIDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`RENAL_CRONICA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`TABAQUISMO` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CLASIFICACION_FINAL` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`FECHA_DEF` is highly correlated with `DIABETES` and 4 other fields	High correlation
`NEUMONIA` is highly correlated with `EDAD` and 11 other fields	High correlation
`EDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`DIABETES` is highly correlated with `FECHA_DEF` and 12 other fields	High correlation
`EPOC` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`ASMA` is highly correlated with `FECHA_DEF` and 12 other fields	High correlation
`INMUSUPR` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`HIPERTENSION` is highly correlated with `FECHA_DEF` and 12 other fields	High correlation
`OTRA_COM` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`CARDIOVASCULAR` is highly correlated with `FECHA_DEF` and 12 other fields	High correlation
`OBESIDAD` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`RENAL_CRONICA` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`TABAQUISMO` is highly correlated with `FECHA_DEF` and 12 other fields	High correlation
`CLASIFICACION_FINAL` is highly correlated with `NEUMONIA` and 11 other fields	High correlation
`FECHA_DEF` is uniformly distributed	Uniform

Reproduction

Analysis started	2021-12-10 07:33:19.947620
Analysis finished	2021-12-10 07:33:37.290489
Duration	17.34 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

FECHA_DEF
Categorical

HIGH CORRELATION
UNIFORM

Distinct	49
Distinct (%)	62.0%
Missing	0
Missing (%)	0.0%
Memory size	5.3 KiB

2020-08-29	2
2020-08-31	2
2020-08-21	2
2020-08-08	2
2020-08-13	2
Other values (44)	69

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	19 ?
Unique (%)	24.1%

Sample

1st row	2020-07-21
2nd row	2020-08-06
3rd row	2020-08-08
4th row	2020-08-08
5th row	2020-08-10

Common Values

Value	Count	Frequency (%)
2020-08-29	2	2.5%
2020-08-31	2	2.5%
2020-08-21	2	2.5%
2020-08-08	2	2.5%
2020-08-13	2	2.5%
2020-08-17	2	2.5%
2020-09-14	2	2.5%
2020-08-16	2	2.5%
2020-08-25	2	2.5%
2020-08-22	2	2.5%
Other values (39)	59	74.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2020-08-29	2	2.5%
2020-09-09	2	2.5%
2020-09-10	2	2.5%
2020-08-15	2	2.5%
2020-08-18	2	2.5%
2020-09-03	2	2.5%
2020-08-30	2	2.5%
2020-08-31	2	2.5%
2020-09-02	2	2.5%
2020-08-28	2	2.5%
Other values (39)	59	74.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

SEXO
Categorical

Distinct	2
Distinct (%)	2.5%
Missing	0
Missing (%)	0.0%
Memory size	4.9 KiB

Mujer	40
Hombre	39

Length

Max length	6
Median length	5
Mean length	5.493670886
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Mujer
2nd row	Mujer
3rd row	Hombre
4th row	Mujer
5th row	Hombre

Common Values

Value	Count	Frequency (%)
Mujer	40	50.6%
Hombre	39	49.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
mujer	40	50.6%
hombre	39	49.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NEUMONIA
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	28
Distinct (%)	35.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.50632911

Minimum	1
Maximum	42
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1.5
median	6
Q3	16
95-th percentile	34.1
Maximum	42
Range	41
Interquartile range (IQR)	14.5

Descriptive statistics

Standard deviation	11.06956259
Coefficient of variation (CV)	1.053608969
Kurtosis	0.5392938306
Mean	10.50632911
Median Absolute Deviation (MAD)	5
Skewness	1.179865868
Sum	830
Variance	122.5352158
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=28)

Value	Count	Frequency (%)
1	20	25.3%
2	9	11.4%
4	6	7.6%
13	4	5.1%
3	4	5.1%
15	3	3.8%
22	3	3.8%
14	2	2.5%
23	2	2.5%
17	2	2.5%
Other values (18)	24	30.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	20	25.3%
2	9	11.4%
3	4	5.1%
4	6	7.6%
6	2	2.5%
7	2	2.5%
8	2	2.5%
9	1	1.3%
11	2	2.5%
12	2	2.5%

Value	Count	Frequency (%)
42	1	1.3%
41	1	1.3%
38	1	1.3%
35	1	1.3%
34	1	1.3%
31	1	1.3%
29	1	1.3%
28	2	2.5%
25	1	1.3%
23	2	2.5%

EDAD
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	73
Distinct (%)	92.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	529.8101266

Minimum	30
Maximum	2240
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	30
5-th percentile	46.9
Q1	77
median	275
Q3	821.5
95-th percentile	1667.9
Maximum	2240
Range	2210
Interquartile range (IQR)	744.5

Descriptive statistics

Standard deviation	564.1244652
Coefficient of variation (CV)	1.064767238
Kurtosis	0.8931951529
Mean	529.8101266
Median Absolute Deviation (MAD)	220
Skewness	1.277319255
Sum	41855
Variance	318236.4122
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
128	2	2.5%
47	2	2.5%
82	2	2.5%
78	2	2.5%
63	2	2.5%
66	2	2.5%
807	1	1.3%
71	1	1.3%
69	1	1.3%
72	1	1.3%
Other values (63)	63	79.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
30	1	1.3%
31	1	1.3%
39	1	1.3%
46	1	1.3%
47	2	2.5%
49	1	1.3%
55	1	1.3%
57	1	1.3%
58	1	1.3%
63	2	2.5%

Value	Count	Frequency (%)
2240	1	1.3%
2028	1	1.3%
1983	1	1.3%
1928	1	1.3%
1639	1	1.3%
1600	1	1.3%
1580	1	1.3%
1425	1	1.3%
1326	1	1.3%
1223	1	1.3%

DIABETES
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	33
Distinct (%)	41.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	16.89873418

Minimum	1
Maximum	219
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	8
Q3	20
95-th percentile	47.3
Maximum	219
Range	218
Interquartile range (IQR)	18

Descriptive statistics

Standard deviation	30.13712037
Coefficient of variation (CV)	1.783395138
Kurtosis	29.10113528
Mean	16.89873418
Median Absolute Deviation (MAD)	7
Skewness	4.927174802
Sum	1335
Variance	908.246024
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
2	13	16.5%
1	12	15.2%
4	5	6.3%
8	4	5.1%
15	3	3.8%
24	3	3.8%
6	3	3.8%
18	3	3.8%
26	2	2.5%
36	2	2.5%
Other values (23)	29	36.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	12	15.2%
2	13	16.5%
3	2	2.5%
4	5	6.3%
5	1	1.3%
6	3	3.8%
7	1	1.3%
8	4	5.1%
9	1	1.3%
10	2	2.5%

Value	Count	Frequency (%)
219	1	1.3%
142	1	1.3%
53	1	1.3%
50	1	1.3%
47	1	1.3%
40	1	1.3%
39	1	1.3%
36	2	2.5%
33	1	1.3%
30	1	1.3%

EPOC
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	36
Distinct (%)	45.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.51898734

Minimum	1
Maximum	225
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	2
Q1	2
median	8
Q3	24
95-th percentile	58.3
Maximum	225
Range	224
Interquartile range (IQR)	22

Descriptive statistics

Standard deviation	28.99861215
Coefficient of variation (CV)	1.565885413
Kurtosis	32.87798973
Mean	18.51898734
Median Absolute Deviation (MAD)	6
Skewness	4.919963919
Sum	1463
Variance	840.9195067
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=36)

Value	Count	Frequency (%)
2	21	26.6%
4	7	8.9%
8	4	5.1%
1	3	3.8%
16	3	3.8%
6	3	3.8%
12	3	3.8%
20	3	3.8%
49	2	2.5%
32	2	2.5%
Other values (26)	28	35.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	3	3.8%
2	21	26.6%
3	1	1.3%
4	7	8.9%
5	1	1.3%
6	3	3.8%
8	4	5.1%
10	1	1.3%
11	1	1.3%
12	3	3.8%

Value	Count	Frequency (%)
225	1	1.3%
66	1	1.3%
64	1	1.3%
61	1	1.3%
58	1	1.3%
49	2	2.5%
46	1	1.3%
43	1	1.3%
42	1	1.3%
38	1	1.3%

ASMA
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	33
Distinct (%)	41.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.73417722

Minimum	2
Maximum	225
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	2
5-th percentile	2
Q1	2
median	8
Q3	24
95-th percentile	60.2
Maximum	225
Range	223
Interquartile range (IQR)	22

Descriptive statistics

Standard deviation	29.1260342
Coefficient of variation (CV)	1.554700474
Kurtosis	32.13529587
Mean	18.73417722
Median Absolute Deviation (MAD)	6
Skewness	4.853425224
Sum	1480
Variance	848.3258682
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
2	24	30.4%
4	7	8.9%
6	4	5.1%
8	4	5.1%
16	3	3.8%
18	2	2.5%
32	2	2.5%
44	2	2.5%
22	2	2.5%
21	2	2.5%
Other values (23)	27	34.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	24	30.4%
3	1	1.3%
4	7	8.9%
6	4	5.1%
8	4	5.1%
10	1	1.3%
11	2	2.5%
12	2	2.5%
13	1	1.3%
16	3	3.8%

Value	Count	Frequency (%)
225	1	1.3%
67	1	1.3%
65	1	1.3%
62	1	1.3%
60	1	1.3%
50	1	1.3%
48	1	1.3%
45	1	1.3%
44	2	2.5%
38	1	1.3%

INMUSUPR
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	31
Distinct (%)	39.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	20.13924051

Minimum	2
Maximum	225
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	2
5-th percentile	2
Q1	2
median	8
Q3	25
95-th percentile	61.5
Maximum	225
Range	223
Interquartile range (IQR)	23

Descriptive statistics

Standard deviation	31.64698478
Coefficient of variation (CV)	1.571409049
Kurtosis	23.35671172
Mean	20.13924051
Median Absolute Deviation (MAD)	6
Skewness	4.194198619
Sum	1591
Variance	1001.531646
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
2	24	30.4%
4	8	10.1%
12	4	5.1%
16	3	3.8%
20	3	3.8%
6	3	3.8%
8	3	3.8%
22	3	3.8%
44	2	2.5%
28	2	2.5%
Other values (21)	24	30.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	24	30.4%
4	8	10.1%
5	1	1.3%
6	3	3.8%
7	1	1.3%
8	3	3.8%
10	1	1.3%
12	4	5.1%
14	1	1.3%
16	3	3.8%

Value	Count	Frequency (%)
225	1	1.3%
128	1	1.3%
68	1	1.3%
66	1	1.3%
61	1	1.3%
60	1	1.3%
50	2	2.5%
46	1	1.3%
44	2	2.5%
38	1	1.3%

HIPERTENSION
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	30
Distinct (%)	38.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	14.65822785

Minimum	1
Maximum	220
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	6
Q3	19.5
95-th percentile	45.1
Maximum	220
Range	219
Interquartile range (IQR)	17.5

Descriptive statistics

Standard deviation	26.61929418
Coefficient of variation (CV)	1.815996753
Kurtosis	46.07508662
Mean	14.65822785
Median Absolute Deviation (MAD)	5
Skewness	6.10499269
Sum	1158
Variance	708.5868225
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=30)

Value	Count	Frequency (%)
1	15	19.0%
2	10	12.7%
3	8	10.1%
6	5	6.3%
17	5	6.3%
12	4	5.1%
23	3	3.8%
46	2	2.5%
9	2	2.5%
24	2	2.5%
Other values (20)	23	29.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	15	19.0%
2	10	12.7%
3	8	10.1%
4	1	1.3%
5	2	2.5%
6	5	6.3%
7	1	1.3%
9	2	2.5%
10	1	1.3%
12	4	5.1%

Value	Count	Frequency (%)
220	1	1.3%
50	1	1.3%
46	2	2.5%
45	1	1.3%
37	1	1.3%
33	2	2.5%
31	1	1.3%
29	2	2.5%
27	1	1.3%
26	1	1.3%

OTRA_COM
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	34
Distinct (%)	43.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	30.63291139

Minimum	1
Maximum	251
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	2
Q1	2
median	8
Q3	29
95-th percentile	156.4
Maximum	251
Range	250
Interquartile range (IQR)	27

Descriptive statistics

Standard deviation	53.44927555
Coefficient of variation (CV)	1.744831722
Kurtosis	6.446475831
Mean	30.63291139
Median Absolute Deviation (MAD)	6
Skewness	2.617370619
Sum	2420
Variance	2856.825057
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=34)

Value	Count	Frequency (%)
2	22	27.8%
4	8	10.1%
6	4	5.1%
12	4	5.1%
32	3	3.8%
7	3	3.8%
20	3	3.8%
16	2	2.5%
46	2	2.5%
1	2	2.5%
Other values (24)	26	32.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	2	2.5%
2	22	27.8%
4	8	10.1%
6	4	5.1%
7	3	3.8%
8	1	1.3%
10	1	1.3%
12	4	5.1%
15	2	2.5%
16	2	2.5%

Value	Count	Frequency (%)
251	1	1.3%
226	1	1.3%
204	1	1.3%
160	1	1.3%
156	1	1.3%
140	1	1.3%
128	1	1.3%
124	1	1.3%
118	1	1.3%
65	1	1.3%

CARDIOVASCULAR
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	34
Distinct (%)	43.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.43037975

Minimum	1
Maximum	226
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	2
Q1	2
median	8
Q3	23.5
95-th percentile	58
Maximum	226
Range	225
Interquartile range (IQR)	21.5

Descriptive statistics

Standard deviation	29.02063083
Coefficient of variation (CV)	1.574608404
Kurtosis	33.51392483
Mean	18.43037975
Median Absolute Deviation (MAD)	6
Skewness	4.979871023
Sum	1456
Variance	842.197014
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=34)

Value	Count	Frequency (%)
2	21	26.6%
4	8	10.1%
21	4	5.1%
1	3	3.8%
20	3	3.8%
8	3	3.8%
28	3	3.8%
12	3	3.8%
5	2	2.5%
6	2	2.5%
Other values (24)	27	34.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	3	3.8%
2	21	26.6%
4	8	10.1%
5	2	2.5%
6	2	2.5%
7	1	1.3%
8	3	3.8%
10	1	1.3%
11	1	1.3%
12	3	3.8%

Value	Count	Frequency (%)
226	1	1.3%
66	1	1.3%
65	1	1.3%
58	2	2.5%
49	2	2.5%
45	1	1.3%
43	1	1.3%
42	1	1.3%
36	1	1.3%
35	1	1.3%

OBESIDAD
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	33
Distinct (%)	41.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	19.08860759

Minimum	1
Maximum	222
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	7
Q3	23
95-th percentile	57.2
Maximum	222
Range	221
Interquartile range (IQR)	21

Descriptive statistics

Standard deviation	33.09705614
Coefficient of variation (CV)	1.733864347
Kurtosis	20.23353931
Mean	19.08860759
Median Absolute Deviation (MAD)	6
Skewness	4.074617517
Sum	1508
Variance	1095.415125
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
2	16	20.3%
1	9	11.4%
3	5	6.3%
14	4	5.1%
18	3	3.8%
6	3	3.8%
7	3	3.8%
36	2	2.5%
28	2	2.5%
26	2	2.5%
Other values (23)	30	38.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	9	11.4%
2	16	20.3%
3	5	6.3%
4	2	2.5%
5	2	2.5%
6	3	3.8%
7	3	3.8%
8	1	1.3%
9	1	1.3%
10	2	2.5%

Value	Count	Frequency (%)
222	1	1.3%
136	1	1.3%
126	1	1.3%
59	1	1.3%
57	1	1.3%
53	1	1.3%
52	1	1.3%
45	1	1.3%
41	1	1.3%
37	1	1.3%

RENAL_CRONICA
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	36
Distinct (%)	45.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	19.44303797

Minimum	1
Maximum	225
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	2
Q1	2
median	8
Q3	24
95-th percentile	60.4
Maximum	225
Range	224
Interquartile range (IQR)	22

Descriptive statistics

Standard deviation	31.64455915
Coefficient of variation (CV)	1.627552196
Kurtosis	24.41420215
Mean	19.44303797
Median Absolute Deviation (MAD)	6
Skewness	4.363639889
Sum	1536
Variance	1001.378124
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=36)

Value	Count	Frequency (%)
2	21	26.6%
4	7	8.9%
16	4	5.1%
1	3	3.8%
8	3	3.8%
12	3	3.8%
22	3	3.8%
6	3	3.8%
27	2	2.5%
30	2	2.5%
Other values (26)	28	35.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	3	3.8%
2	21	26.6%
3	1	1.3%
4	7	8.9%
5	1	1.3%
6	3	3.8%
7	1	1.3%
8	3	3.8%
10	1	1.3%
11	1	1.3%

Value	Count	Frequency (%)
225	1	1.3%
137	1	1.3%
64	2	2.5%
60	1	1.3%
57	1	1.3%
49	1	1.3%
48	1	1.3%
42	1	1.3%
41	1	1.3%
35	1	1.3%

TABAQUISMO
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	38
Distinct (%)	48.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	19.48101266

Minimum	1
Maximum	223
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	1
5-th percentile	2
Q1	2
median	8
Q3	24
95-th percentile	58.2
Maximum	223
Range	222
Interquartile range (IQR)	22

Descriptive statistics

Standard deviation	31.34704859
Coefficient of variation (CV)	1.609107757
Kurtosis	24.2781373
Mean	19.48101266
Median Absolute Deviation (MAD)	6
Skewness	4.338340509
Sum	1539
Variance	982.6374554
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=38)

Value	Count	Frequency (%)
2	23	29.1%
4	7	8.9%
8	4	5.1%
20	3	3.8%
28	2	2.5%
34	2	2.5%
5	2	2.5%
6	2	2.5%
24	2	2.5%
11	2	2.5%
Other values (28)	30	38.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1	1.3%
2	23	29.1%
3	1	1.3%
4	7	8.9%
5	2	2.5%
6	2	2.5%
8	4	5.1%
10	1	1.3%
11	2	2.5%
12	2	2.5%

Value	Count	Frequency (%)
223	1	1.3%
135	1	1.3%
67	1	1.3%
60	1	1.3%
58	1	1.3%
57	1	1.3%
49	1	1.3%
45	1	1.3%
42	1	1.3%
40	1	1.3%

CLASIFICACION_FINAL
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	34
Distinct (%)	43.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	24.36708861

Minimum	2
Maximum	97
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	760.0 B

Quantile statistics

Minimum	2
5-th percentile	3
Q1	3
median	12
Q3	37.5
95-th percentile	76.4
Maximum	97
Range	95
Interquartile range (IQR)	34.5

Descriptive statistics

Standard deviation	25.61008276
Coefficient of variation (CV)	1.051011188
Kurtosis	0.7899258645
Mean	24.36708861
Median Absolute Deviation (MAD)	9
Skewness	1.250589739
Sum	1925
Variance	655.8763389
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=34)

Value	Count	Frequency (%)
3	22	27.8%
6	7	8.9%
24	3	3.8%
48	3	3.8%
9	3	3.8%
12	3	3.8%
33	3	3.8%
18	3	3.8%
30	3	3.8%
2	2	2.5%
Other values (24)	27	34.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	2	2.5%
3	22	27.8%
5	1	1.3%
6	7	8.9%
8	1	1.3%
9	3	3.8%
10	1	1.3%
12	3	3.8%
15	1	1.3%
16	1	1.3%

Value	Count	Frequency (%)
97	2	2.5%
89	2	2.5%
75	1	1.3%
74	1	1.3%
67	1	1.3%
65	1	1.3%
61	1	1.3%
56	1	1.3%
53	1	1.3%
51	1	1.3%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	FECHA_DEF	SEXO	NEUMONIA	EDAD	DIABETES	EPOC	ASMA	INMUSUPR	HIPERTENSION	OTRA_COM	CARDIOVASCULAR	OBESIDAD	RENAL_CRONICA	TABAQUISMO	CLASIFICACION_FINAL
0	2020-07-21	Mujer	1	66	1	2	2	2	1	2	2	1	2	2	2
1	2020-08-06	Mujer	1	39	1	2	2	2	1	2	2	1	2	2	3
2	2020-08-08	Hombre	1	94	2	1	2	2	1	2	1	2	2	2	3
3	2020-08-08	Mujer	1	71	1	2	2	2	1	2	2	2	2	2	3
4	2020-08-10	Hombre	1	66	2	2	2	2	2	2	2	2	2	2	2
5	2020-08-11	Hombre	1	57	1	2	2	2	1	2	1	2	2	2	3
6	2020-08-12	Hombre	1	63	1	2	2	2	2	2	2	1	2	2	3
7	2020-08-13	Hombre	4	275	7	8	8	8	5	7	8	7	8	8	12
8	2020-08-13	Mujer	2	63	1	2	2	2	1	2	2	1	2	2	3
9	2020-08-14	Hombre	3	213	5	6	6	6	3	6	5	6	5	6	8

Last rows

	FECHA_DEF	SEXO	NEUMONIA	EDAD	DIABETES	EPOC	ASMA	INMUSUPR	HIPERTENSION	OTRA_COM	CARDIOVASCULAR	OBESIDAD	RENAL_CRONICA	TABAQUISMO	CLASIFICACION_FINAL
69	2020-09-15	Hombre	3	163	6	6	6	6	5	6	6	6	6	5	9
70	2020-09-16	Hombre	2	31	2	2	2	2	1	2	2	2	1	2	3
71	2020-09-20	Mujer	2	130	2	4	4	4	3	4	4	3	4	4	5
72	2020-09-26	Hombre	2	128	4	4	4	4	2	4	4	4	4	4	6
73	2020-10-06	Hombre	1	69	2	2	2	2	2	2	2	2	2	2	3
74	2020-10-08	Mujer	1	78	1	1	2	2	1	2	2	1	2	2	3
75	2020-10-11	Mujer	1	46	2	1	2	2	2	2	2	1	2	1	3
76	2020-10-28	Mujer	1	47	1	2	2	2	2	2	2	1	2	2	3
77	2020-12-15	Mujer	1	30	2	2	2	2	2	2	2	2	1	2	3
78	2020-12-17	Hombre	1	58	1	2	2	2	1	2	1	2	1	2	3

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows